# Data + AI Observability For Databricks Monte Carlo’s Data + AI Observability Platform gives your team full visibility into the health of your AI systems — from data input to agent output — natively on Databricks’s Data Intelligence Platform. ## Validated at Every Level of the Databricks Ecosystem The highest tiers of technical validation and partner recognition from Databricks — so your team can buy and deploy with confidence. ### 2025 Data Governance Partner of the Year Officially awarded by Databricks for innovation, joint customer success, and leadership in data + AI observability across the Data Intelligence Platform. ### Databricks Partner Connect Available directly in Databricks Partner Connect — integrate Monte Carlo into your lakehouse in a few clicks with no manual configuration required. ### Unity Catalog Native Integration First end-to-end observability platform to integrate with Delta Lake and Unity Catalog across all endpoints — down to the BI layer. ### Mosaic AI & AgentBricks Observability Native observability for Databricks Mosaic AI agents and AgentBricks — monitor AI agent inputs, behavior, and outputs end to end. ### AI/BI Integration Monitor the quality of data underpinning Databricks AI/BI insights with AI-powered anomaly detection and automated root cause analysis. ### Industry Competency Badges Recognized across Financial Services, Healthcare, Retail, Media, and Technology for verified customer success. ## Observability at Every Layer of Your Lakehouse Monte Carlo covers the full journey — from raw data in Delta Lake through Unity Catalog to what your Mosaic AI agents produce. ### Lakehouse Observability Automated monitoring across Delta Lake, Unity Catalog, and all Databricks pipelines. ### Automated Anomaly Detection ML-powered monitors learn your data patterns across Delta tables and flag deviations in volume, freshness, schema, and distributions automatically. ### End-to-End Lineage Column-level lineage from ingestion through Databricks Workflows to every downstream BI tool, AI model, and Mosaic AI agent — zero instrumentation needed. ### Unity Catalog Metrics Monitoring Monitor the integrity of Unity Catalog Metrics definitions, ensuring key business KPIs remain accurate and consistent across domains and dashboards. ### Databricks Workflows Integration Correlate data anomalies directly to the specific Databricks Workflow or task that caused the issue — enabling faster, full-lifecycle incident resolution. ## Agent Layer ### AI Agent Reliability Input validation and context reliability for Mosaic AI and AgentBricks agents. ### Pre-flight Data Validation Monitor the Delta tables your Mosaic AI agents retrieve from. Catch stale, incomplete, or anomalous data before it reaches the agent context window. ### RAG Pipeline Observability Monitor unstructured data powering LLMs and RAG pipelines in Databricks — detect anomalies in documents, chat logs, and embeddings before they degrade agent quality. ### AgentBricks Integration Native integration with Databricks AgentBricks — monitor agent inputs, behavior, and outputs without custom instrumentation or code changes. ### Unstructured Data Monitoring First platform to monitor both structured and unstructured data in Databricks — detect sentiment shifts, missing text, and format anomalies in AI-feeding datasets. ## Output Layer ### AI Output Observability Monitor what agents produce and trace failures back to root cause in your lakehouse. ### Agent Output Monitoring Track what your Mosaic AI and AI/BI agents produce over time — detecting drift, degradation, or unexpected behavior before it reaches customers. ### Root Cause Tracing When an agent misbehaves, Monte Carlo traces the failure through the full lakehouse stack — from agent output to the specific Delta table or pipeline that caused it. ### Incident Routing Route AI-related incidents to the right owner instantly via Slack, Teams, PagerDuty, and Jira — with automatic blast radius scoping across all consumers. ### SLA & Reliability Tracking Set reliability targets for your AI systems. Track data SLAs, agent uptime, and input quality trends to demonstrate AI readiness to leadership. ### If the Data is Wrong, the Agent is Wrong. Monte Carlo is the first observability platform built to monitor the full Mosaic AI and AgentBricks agent loop — from data input through agent output — so your team catches failures before customers do. - Monitor Delta Lake data quality before agents consume it - Trace every agent decision back to its lakehouse source - Observe RAG pipelines and unstructured data inputs end to end - Works natively with Mosaic AI, AgentBricks, and Databricks AI/BI ### Customer Stories ## Reliability Designed for the Enterprise Our customers scale trust, reduce risk, and deliver better business outcomes. See how you can too. ### How Axios Is Delivering Reliable AI with Agent Observability #### The Challenge: Axios needed to monitor across their data + AI lifecycle including agent context, performance, behavior, and outputs. #### The Solution: Leveraged Agent Observability for full agent visibility integrated into a robust incident management workflow. ### How JetBlue Improved Internal “Data NPS” By 16 Points YoY #### The Challenge: When a data migration improved data usage, increased access brought increased scrutiny. And the trustworthiness of the data took center stage. #### The Solution: Operationalizing data + AI observability and leveraging Monte Carlo's in-app features to measure the outcomes. ### Nasdaq's Journey to Reliability with Monte Carlo #### The Challenge: Nasdaq generates 6,000 reports per day across 35 services and 2,200 users. The question is—how do you make that much data reliable? #### The Solution: The team deployed Monte Carlo to monitor its entire data lake via a multi-step deployment.